The research aims to build software that can perform the classification of earth image from UAV (Unmanned Aerial Vehicle) monitoring. The Image converted into YUV format then classified using Fuzzy Support Vector Mach...
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This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In thi...
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ISBN:
(纸本)9781479914821
This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In this formulation, an extra tensor mode is formed by a collection of tensors of the same dimensions and then used to assist a Tucker decomposition in order to achieve data dimensionality reduction. We design two types of clustering models for the tensors: PCA Tensor Clustering model and Non-negative Tensor Clustering model, by utilizing different regularizations. The tensor clustering can thus be solved by the optimization method based on the alternative coordinate scheme. Interestingly, our experiments show that the proposed models yield comparable or even better performance compared to most recent clustering algorithms based on matrix factorization.
In this paper, a novel image blocky artifact removal scheme based on low-rank matrix recovery is proposed. The problem of suppressing blocky artifacts is formulated as recovering a low-rank matrix from corrupted obser...
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ISBN:
(纸本)9781479928941
In this paper, a novel image blocky artifact removal scheme based on low-rank matrix recovery is proposed. The problem of suppressing blocky artifacts is formulated as recovering a low-rank matrix from corrupted observations. During the deblocking processing, we do not directly recover the whole clean image but only its high-frequency component and then synthesize the clean image by incorporating the low-frequency component of blocky image. To take advantage of the low-rank matrix recovery paradigm, we first cluster the similar patches of the high-frequency component of image via local pixel clustering, then the clean high-frequency component of image is recovered by formulating an optimization problem of the nuclear norm and l_1-norm. The experimental results show that the proposed algorithm can achieve competitive performance in terms of both quantitative and subjective quality.
3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually improved, the performance of existing...
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With the high frequent use of social applications on Android platform, the cache file privacy disclosure issues have become increasingly serious. To our best knowledge, there is no effective privacy protection solutio...
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ISBN:
(纸本)9781509035403
With the high frequent use of social applications on Android platform, the cache file privacy disclosure issues have become increasingly serious. To our best knowledge, there is no effective privacy protection solution for social applications cache files. In this paper, we analyze the present situation of social applications cache file leaks on Android platform, and provide a privacy disclosure assessment criterion based on file storage directories and security state machines. And a cache file privacy protection framework, X-Prcaf (Xposed-based-Protecting-Cache-File), is proposed, which can make social applications avoid privacy data leaks in running process. This framework mainly uses taint tracking technology, operating system hook technology, and cryptographic technology. It aims to protect the entire life cycle of the social applications cache files, by strategy pre-generation, real-time monitoring and security reinforcement. Experiments demonstrate that X-Prcaf has a good effect on the cache file leaks of social software.
Sentence alignment, as one of the most active and fundamental tasks in the field of natural language processing (NLP), is usually realized in two categories of methods. One is traditional methods which are firstly pro...
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ISBN:
(纸本)9781450377607
Sentence alignment, as one of the most active and fundamental tasks in the field of natural language processing (NLP), is usually realized in two categories of methods. One is traditional methods which are firstly proposed, the other, which are adopted later, is based on the Neural Network method. Presently, under the limitation that the existing mainstream data corpora are mostly in the form of 1-to-1, the alignment models with relatively good performance mainly apply to the cases of 1-to-1 sentence alignment. However, under the circumstance that a sentence contains too much information, 1-to-N sentence alignment can actually have a better effect on sentence translation tasks, compared with the 1-to-1 form, since it is more flexible and can reduce the complexity of the original sentence. As a result, we attempt to exploit neural networks with relatively good performance in the cases of 1-to-1 to fit in the cases of 1-to-N. In this paper, a novel 1-N Bilingual word Embedding with Sentence Combination CNN Improved Framework (1-NBESCC) is proposed in order to align 1-to-N sentences more precisely. Experiments show that our proposed model performs as good as the traditional methods such as BLEUALIGN in 1-to-1 situation, but much better in 1-to-N situation.
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